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combinatorial, algebraic, and probabilistic tools, you will work on resolving fundamental extremal problems in the area of finite geometry. These problems have connections to Ramsey theory, coding theory, and
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algebra. Desirable: PDEs/numerics, thermofluids/heat transfer, uncertainty quantification, and hands-on lab skills. Prior publications or open-source contributions are a plus but not required. Eligibility
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expertise will extend to various areas, including quantum Monte Carlo, machine learning, quantum computing, quantum machine learning, and tensor networks. These and other techniques will allow us to confront
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the Numerical Analysis group at TU Delft. The group is internationally recognized for its contributions to iterative methods, numerical linear algebra, and parallel computing. The project will be carried out in
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Scientist position in the areas of computational algebra, geometry, and topology. The successful candidate is expected to be active in research and to teach courses related to the focus of the advertised
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, or neural networks). Non-negative tensor factorization will also be applied to identify patterns in the high-dimensional mobility datasets. Social network analysis will characterize relationships between
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of invariants of operator algebras, such as K-theory and cyclic homology; and Developing a mathematical method for passing from numerical Berry curvature to robust topological invariants in a large class of cases
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) Strong programming and quantitative skills, including coursework in multivariable calculus, matrix algebra, probability, and statistics (required) Previous experience estimating econometric models
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Languages (ACP ), the section on Topology, Algebra, Analysis and Geometry (TAAG ) and the Centre for Quantum Mathematics (QM ). The proposed starting time is Winter 2026, with some room for flexibility
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: Algebraic geometry and number theory Area 3: Stochastics and mathematical finance Area 4: Discrete mathematics and optimization Area 5: Discrete geometry Area 6: Numerical mathematics Area 7: Applied analysis